Rankings / OpenAI
O3 (2025-04-16)
released 2025-04-16
BenchAtlas Index
as of 2026-07-14
high effort
rank #138
9 families · 4 categories · medium
high effort
rank #140
9 families · 5 categories · high
base configuration
rank #146
11 families · 5 categories · high
Benchmark evidence
88 results
agentic coding
| τ²-Bench | 80.7 % | independent | Artificial Analysis |
coding
| LiveCodeBench v6 implementation=artificial-analysis | 80.8 % | independent | Artificial Analysis | |
| LiveCodeBench v6 problems=454 · window_start=2024-08-01 | high effort | 75.8 % | independent | LiveCodeBench Leaderboard |
| SciCode | 41.0 % | independent | Artificial Analysis |
external indices
| Intelligence Index v4.1 | 30.4 points | independent | Artificial Analysis | |
| AA Math Index | 88.3 points | independent | Artificial Analysis | |
| ECI | low effort | 147.1 points | independent | Epoch AI Benchmarking Hub |
| ECI | 147.1 points | independent | Epoch AI Benchmarking Hub | |
| ECI | medium effort | 147.1 points | independent | Epoch AI Benchmarking Hub |
| ECI | high effort | 147.1 points | independent | Epoch AI Benchmarking Hub |
factuality
| SimpleQA Verified | high effort | 53.0 % | independent | Epoch AI Benchmarking Hub 2025-12-09 |
human preference
| French (style control)older version arena=text · category=french · style_control=true | 1475.0 1451.8–1498.1 | community | LMArena Leaderboard Dataset | |
| Industry Medicine And Healthcare (style control)older version arena=text · category=industry_medicine_and_healthcare · style_control=true | 1474.8 1463.7–1486.0 | community | LMArena Leaderboard Dataset | |
| Chinese (style control)older version arena=text · category=chinese · style_control=true | 1464.4 1453.0–1475.8 | community | LMArena Leaderboard Dataset | |
| Polish (style control)older version arena=text · category=polish · style_control=true | 1459.1 1447.8–1470.4 | community | LMArena Leaderboard Dataset | |
| Coding (style control)older version arena=text · category=coding · style_control=true | 1458.9 1452.7–1465.1 | community | LMArena Leaderboard Dataset | |
| Industry Life And Physical And Social Science (style control)older version arena=text · category=industry_life_and_physical_and_social_science · style_control=true | 1453.8 1447.0–1460.5 | community | LMArena Leaderboard Dataset | |
| Industry Software And It Services (style control)older version arena=text · category=industry_software_and_it_services · style_control=true | 1453.4 1448.2–1458.6 | community | LMArena Leaderboard Dataset | |
| Industry Legal And Government (style control)older version arena=text · category=industry_legal_and_government · style_control=true | 1451.0 1441.0–1461.2 | community | LMArena Leaderboard Dataset | |
| Industry Mathematical (style control)older version arena=text · category=industry_mathematical · style_control=true | 1448.8 1438.5–1459.1 | community | LMArena Leaderboard Dataset | |
| Math (style control)older version arena=text · category=math · style_control=true | 1447.4 1437.5–1457.3 | community | LMArena Leaderboard Dataset | |
| Hard Prompts English (style control)older version arena=text · category=hard_prompts_english · style_control=true | 1445.8 1440.0–1451.7 | community | LMArena Leaderboard Dataset | |
| Expert (style control)older version arena=text · category=expert · style_control=true | 1444.6 1433.1–1456.0 | community | LMArena Leaderboard Dataset | |
| Hard Prompts (style control)older version arena=text · category=hard_prompts · style_control=true | 1439.9 1435.1–1444.7 | community | LMArena Leaderboard Dataset | |
| German (style control)older version arena=text · category=german · style_control=true | 1438.6 1422.0–1455.1 | community | LMArena Leaderboard Dataset | |
| English (style control)older version arena=text · category=english · style_control=true | 1437.9 1433.3–1442.4 | community | LMArena Leaderboard Dataset | |
| Overall (style control) arena=text · category=overall · style_control=true | 1431.0 1427.3–1434.6 | community | LMArena Leaderboard Dataset | |
| Russian (style control)older version arena=text · category=russian · style_control=true | 1430.4 1420.8–1440.1 | community | LMArena Leaderboard Dataset | |
| Japanese (style control)older version arena=text · category=japanese · style_control=true | 1427.2 1408.9–1445.4 | community | LMArena Leaderboard Dataset | |
| Industry Business And Management And Financial Operations (style control)older version arena=text · category=industry_business_and_management_and_financial_operations · style_control=true | 1425.3 1418.4–1432.1 | community | LMArena Leaderboard Dataset | |
| Exclude Ties (style control)older version arena=text · category=exclude_ties · style_control=true | 1423.9 1418.9–1428.9 | community | LMArena Leaderboard Dataset | |
| Non English (style control)older version arena=text · category=non_english · style_control=true | 1423.7 1419.0–1428.3 | community | LMArena Leaderboard Dataset | |
| Multi Turn (style control)older version arena=text · category=multi_turn · style_control=true | 1418.6 1411.9–1425.4 | community | LMArena Leaderboard Dataset | |
| Longer Query (style control)older version arena=text · category=longer_query · style_control=true | 1408.2 1402.0–1414.4 | community | LMArena Leaderboard Dataset | |
| Spanish (style control)older version arena=text · category=spanish · style_control=true | 1404.1 1384.7–1423.6 | community | LMArena Leaderboard Dataset | |
| Instruction Following (style control)older version arena=text · category=instruction_following · style_control=true | 1401.8 1396.3–1407.4 | community | LMArena Leaderboard Dataset | |
| Industry Writing And Literature And Language (style control)older version arena=text · category=industry_writing_and_literature_and_language · style_control=true | 1396.2 1390.3–1402.1 | community | LMArena Leaderboard Dataset | |
| Industry Entertainment And Sports And Media (style control)older version arena=text · category=industry_entertainment_and_sports_and_media · style_control=true | 1394.8 1388.4–1401.2 | community | LMArena Leaderboard Dataset | |
| Korean (style control)older version arena=text · category=korean · style_control=true | 1393.1 1374.4–1411.7 | community | LMArena Leaderboard Dataset | |
| Creative Writing (style control)older version arena=text · category=creative_writing · style_control=true | 1383.2 1376.0–1390.5 | community | LMArena Leaderboard Dataset |
knowledge science
| GPQA Diamond implementation=vals-ai | high effort | 84.1 % | independent | Vals AI |
| GPQA Diamond implementation=artificial-analysis | 82.7 % | independent | Artificial Analysis | |
| GPQA Diamond | high effort | 81.8 % | independent | Epoch AI Benchmarking Hub 2025-04-16 |
| Humanity's Last Exam implementation=artificial-analysis | 20.0 % | independent | Artificial Analysis | |
| MMLU-Pro implementation=vals-ai | high effort | 85.6 % | independent | Vals AI |
| MMLU-Pro implementation=artificial-analysis | 85.3 % | independent | Artificial Analysis |
long context instruction
| AA-LCR | 69.3 % | independent | Artificial Analysis | |
| IFBench | 71.4 % | independent | Artificial Analysis |
multimodal
| MMMU implementation=vals-ai | high effort | 80.4 % | independent | Vals AI |
professional
| CorpFin | high effort | 59.7 % | independent | Vals AI |
| LegalBench | high effort | 83.8 % | independent | Vals AI |
| MedQA | high effort | 96.1 % | independent | Vals AI |
| TaxEval | high effort | 74.6 % | independent | Vals AI |
reasoning math
| AIME implementation=artificial-analysis | 90.3 % | independent | Artificial Analysis | |
| AIME year=2025 · implementation=artificial-analysis | 88.3 % | independent | Artificial Analysis | |
| AIME implementation=vals-ai | high effort | 85.3 % | independent | Vals AI |
| ARC-AGI-1older version split=public_eval · model_type=CoT | high effort | 64.3 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | high effort | 60.8 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | medium effort | 56.7 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | medium effort | 53.8 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | low effort | 47.6 % | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | low effort | 41.5 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | high effort | 6.5 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | medium effort | 4.5 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | medium effort | 3.0 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | high effort | 2.9 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | low effort | 2.7 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | low effort | 2.0 % | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | high effort | 0.9 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | high effort | 0.8 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | high effort | 0.5 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | medium effort | 0.5 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | medium effort | 0.5 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | high effort | 0.4 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | medium effort | 0.3 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | medium effort | 0.3 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=public_eval · model_type=CoT | low effort | 0.2 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-2 split=semi_private · model_type=CoT | low effort | 0.2 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=semi_private · model_type=CoT | low effort | 0.2 usd_per_task | independent | ARC Prize Leaderboard |
| ARC-AGI-1older version split=public_eval · model_type=CoT | low effort | 0.2 usd_per_task | independent | ARC Prize Leaderboard |
| FrontierMath | high effort | 18.7 % | independent | Epoch AI Benchmarking Hub 2025-11-16 |
| FrontierMath | medium effort | 16.9 % | independent | Epoch AI Benchmarking Hub 2025-11-16 |
| FrontierMath | low effort | 9.7 % | independent | Epoch AI Benchmarking Hub 2025-11-17 |
| FrontierMath Tier 4older version | high effort | 2.1 % | independent | Epoch AI Benchmarking Hub 2025-07-01 |
| MATH-500 implementation=artificial-analysis | 99.2 % | independent | Artificial Analysis | |
| MATH-500 implementation=vals-ai | high effort | 94.6 % | independent | Vals AI |
| MATH Level 5 | high effort | 97.8 % | independent | Epoch AI Benchmarking Hub 2025-04-16 |
| OTIS Mock AIME 2024–2025 | high effort | 83.9 % | independent | Epoch AI Benchmarking Hub 2025-04-16 |
Agent + model results
systems, not bare-model scores
| agent + model Epoch Inspect harness + O3 (2025-04-16) | SWE-bench Verified | 62.3 % | independent | Epoch AI Benchmarking Hub |
| agent + model Codefuse_Pycfuse_SVR + O3 (2025-04-16) | SWE-bench Multimodal | 36.0 % | community | SWE-bench Leaderboard |
| agent + model GUIRepair + O3 (2025-04-16) | SWE-bench Multimodal | 36.0 % | community | SWE-bench Leaderboard |
| agent + model Artificial Analysis harness + O3 (2025-04-16) | Terminal-Bench Hard | 37.1 % | independent | Artificial Analysis |
| agent + model terminus-1 + O3 (2025-04-16) | Terminal-Bench 1.0 | 30.2 % | community | Terminal-Bench Leaderboard |
These scores measure the whole agent system (scaffold, tools, budgets) — they are never merged into the bare model’s numbers.
